def forward(self, logits: torch.Tensor, labels: torch.Tensor):
assert logits.ndim == 2, "(B, F)"
assert labels.ndim == 1, "(B, )"
with torch.no_grad():
if self.average == 'macro':
return precision(pred=nn.functional.softmax(logits, dim=1),
target=labels,
num_classes=self.num_classes,
reduction='elementwise_mean')
elif self.average == 'micro':
raise NotImplementedError
elif self.average == 'weighted':
raise NotImplementedError
else:
raise ValueError
def test_precision_recall_joint(average):
"""A simple test of the joint precision_recall metric.
No need to test this thorougly, as it is just a combination of precision and recall,
which are already tested thoroughly.
"""
precision_result = precision(
_input_mcls_prob.preds[0], _input_mcls_prob.target[0], average=average, num_classes=NUM_CLASSES
)
recall_result = recall(
_input_mcls_prob.preds[0], _input_mcls_prob.target[0], average=average, num_classes=NUM_CLASSES
)
prec_recall_result = precision_recall(
_input_mcls_prob.preds[0], _input_mcls_prob.target[0], average=average, num_classes=NUM_CLASSES
)
assert torch.equal(precision_result, prec_recall_result[0])
assert torch.equal(recall_result, prec_recall_result[1])